On ZnO nano particle reinforced PVDF composite materials for 3D printing of biomedical sensors

Journal of Manufacturing Processes 60 (2020) 268–282

Contents lists available at ScienceDirect

Journal of Manufacturing Processes journal homepage: www.elsevier.com/locate/manpro

Technical Paper

On ZnO nano particle reinforced PVDF composite materials for 3D printing of biomedical sensors Ranvijay Kumar a, Rupinder Singh b, c, *, Mohit Singh b, Pawan Kumar d a

University Center for Research and Development, Chandigarh University, India Department of Production Engineering, Guru Nanak Dev Engineering College, Ludhiana, India c Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India d Department of Physics, Chandigarh University, India b

A R T I C L E I N F O

A B S T R A C T

Keywords: PVDF ZnO reinforcement Shape memory effects Biosensors printing

The inherent piezoelectric and biocompatibility behaviour of polyvinylidene difluoride (PVDF) and excellent biocompatibility, thermal and magnetic responses of zinc oxide (ZnO) nano particles made their blends as a good candidate for biosensor 3D printing. In this study, the co-precipitation method has been used to prepare the nanosized ZnO particles, which are further reinforced with PVDF on twin screw compounder with the controlling proportion of ZnO, forced loading, and torque. The investigations of mechanical, thermal, morphological, and shape memory effect have been performed on the prepared feedstock filaments to check the effect of controlling parameters. It has been observed that maximum mechanical strength (tensile strength, percentage elongation and modulus of toughness) results without ZnO nano particle reinforcement, 1% reinforced critically reduced the mechanical properties but the mechanical properties of 2% ZnO nanoparticle reinforced PVDF composite were obtained better than PVDF-1%ZnO and less than PVDF-0%ZnO. Thermal analysis shows that increase in the ZnO percentage into PVDF improves the normalized heat capacity (25.16 J/g in PVDF to 33.07 J/g in PVDF-2%ZnO). The energy dispersive spectroscopy (EDS) analysis confirms the presence of ZnO on the fracture surface. The shape memory was found 98.22 % for PVDF-2%ZnO at 25 ◦ C.

Introduction Researchers and academicians are getting attention to utilize poly­ mer composites due to their sustainability, nature, and economic utility [1]. Polymers are the essential ingredient of the 3D printing process based upon additive manufacturing like; fused deposition modeling (FDM). Various metal oxide fillers like; nano-sized ZnO, zirconium oxide (ZrO2), and cerium oxide (CeO2) is used as a reinforcement in different polymers because of the unique physical properties as well as their low cost and extensive applications in 3D printing. ZnO is a wide bandgap (3.37 eV) semiconductor material, which has been studied extensively in the last few decades due to its unique optical, magnetic, and electrical properties for various applications. ZnO has been functionalized in different size and morphology (nano rod, nano ring, nano wire, nano flower, and nano particles, etc.) due to its applications in optoelectronics device (UV detector, solar cell, sensors, LED), rubber industry (fillers, activators of rubber compound), pharmaceutical industry (a component of cream, powder and dental paste) textile industry (absorber of UV

radiation) and spintronics devices (spin-polarized LED’s, spin transis­ tors). In addition, its biocompatibility leads to several other applications such as Photocatalyst, biosensors, packing of food products etc. It was found that ZnO is the best suitable candidate for doping of transition metal and rare earth metals to improve the performance of ZnO based devices and explore new applications [2–5]. ZnO has also been used as an additive to improve the property (mechanical, surface, and thermal) of various polymers. The polymeric materials most frequently used in FDM are poly­ carbonate (PC), polylactic acid (PLA), polyvinylidene difluoride (PVDF), Polyphenylsulfone (PPSF), acrylonitrile butadiene styrene (ABS) and their blends. Some studies have reported reinforcement of steel powders, aluminium powder and even wood dust, ash etc. in thermoplastics for enhancing mechanical, thermal properties [6–7]. PVDF is an attractive material having high piezoelectric and pyroelectric behaviour which leads to applications in sensors [9,10], infrared detectors [11], trans­ ducers [12], energy harvester [13], actuators [14,15] and micro-electro-mechanical systems (MEMS) [16]. A study on ZnO

* Corresponding author. E-mail address: [email protected] (R. Singh). https://doi.org/10.1016/j.jmapro.2020.10.027 Received 19 November 2019; Received in revised form 7 June 2020; Accepted 7 October 2020 1526-6125/© 2020 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Fig. 1. Synthesis of ZnO, compound preparation, filament extrusion and 3D printing of functional prototypes.

nano-rods revealed that the increase in the active surface area helps for the increase of the photocatalytic properties [17]. It has reported that a combination of the FDM technique and hydrothermal reaction effec­ tively fabricates the polymer-ZnO structures [18]. The proposed ZnO modification reveals that there was no significant change in the me­ chanical properties of ZrO2 ceramics. Whereas the ceramics modified with ZnO exhibited significantly change, i.e higher antibacterial effi­ ciency than pure ZrO2 ceramics [19]. The nano composite polystyrene filaments enriched with ZnO and TiO2 nanoparticles up to 20 % wt, enhance photo-catalytic properties, and helps to reach an efficiency of almost 70% after five cycles of reuse in 20 ppm of Methylene [20]. The study suggested that the deposition of ZnO in ZrO2 can increase anti­ microbial activities [21]. In the case of fabrication of biomedical scaf­ folds, it has been studies that were increasing the ZnO concentration from 0 wt% to 2.5 wt% results in improved mechanical and biological properties [22]. Twin screw extrusion (TSE) is used for the uniform dispersion of the particles/fillers in the polymer matrix. It has been re­ ported that varying the TSE process parameters results in the influenced mechanical, thermal, and morphological properties [23]. The previous study suggests that dispersion and air asperities defect can be controlled by using TSE [24]. Now a day’s PVDF material has been used for the applications of the antibacterial product. The high antibacterial (PVDF-HFP fibrous membrane) material developed for both gram-positive and gram-negative bacteria characteristics, by intro­ ducing poly(4-vinyl-N-alkyl pyridinium bromide) on the surface via UV-induced graft copolymerization and quaternization. The modified PVDF-HFP fibrous membranes showed no significant morphological change, but some minor changes have been observed in the mechanical properties as compared with pristine membranes [25]. The PVDF rein­ forced reduced graphene oxide (RGO)-ZnO nano composites show higher thermal stability than the pure polymer [26]. Poly (methyl methacrylate) (PMMA) with reinforcement of ZnO nanoparticles improved the thermal stability, which has been identified by thermog­ ravimetric analysis (TGA). Also, it has been determined that the copo­ lymerization and grafting reaction does not alter the crystalline structure of the ZnO nanoparticles, according to as confirmed by X-ray diffraction patterns [27]. The shape memory materials have special features to recover the original structures with the application of external stimuli [28–30].

These external stimuli can be light, pressure, heat, pH, electric and magnetic fields etc. The previous studies have reported that heating/­ reheating is one of the most effective stimuli for shape memory effects [31–33]. It has been observed that loading of 15 wt% hydroxyapatite (HAp) loading into the PLA matrix shows 98% shape recovery, and this can be applied for the healing of biomedical implants [34–35]. The shape memory materials are the key for performing the four-dimensional (4D) printing. The shape memory materials provide the 4D capabilities (by incorporating the abilities to change the shape by application of external stimuli) in the 3D printed parts [36]. The 3D printed component having SME with lightweight structure may be used for self-shaping, self-folding, and self-unfolding performances of biomedical scaffolds/implants [37–39]. The shape memory materials are the input for the 4D printing of biosensors. The biosensor is the smart device that can sense the changes in the biological elements such as; tissues, proteins, nucleic acids, enzymes, microorganism concentration, biological oxygen demands, antibodies etc [40]. Piezoelectricity, thermal stability, and biocompatibility are major advantages of PVDF with ease of great mouldability for 3D printing of sensors, detectors, transducers, energy harvester, actuators and MEMS [8,10–16,25]. ZnO nanoparticles have a tendency to respond ferro­ electricity, photocatalytic, optical changes, and ultraviolet radiations with good antibacterial and antifungal properties [2,18–19,27]. Some studies have been reported for 3D printing of polymer-metallic oxide combinations for various applications [10–16,26]. But hitherto very less has been reported for the preparations of feedstock filaments of 3D printing for ZnO nanoparticles reinforced PVDF composite materials for biosensors /biomedical sensors. To the best of our knowledge, this is the first report for enhancement of shape memory effect of the functional prototypes of PVDF reinforced with ZnO. In this study, feedstock fila­ ments were prepared on twin screw compounder, and further thermal, mechanical, morphological, and shape memory effect have been inves­ tigated for applications in biosensors/biomedical sensors. Materials and Methods The PVDF and ZnO are having good antibacterial properties, so that these materials have been selected for experimentation. The antimi­ crobial activities of the biosensors are determined against the Gram269

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

positive and Gram-negative characteristics. It has been established by the previous studies that decrease in size of ZnO nano particles have increased the antibacterial activities against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) bacteria [41]. Also, the PVDF material has the ability to restrict the growth of microbial species [42]. Based on these facts, the ZnO and PVDF have been selected for the present study. ZnO nano particles were synthesized by well-established co-precipitation method. Zinc acetate dehydrate (Zn (CH3COO)2.2H2O) was used as a precursor and source of Zn+2 ions during ZnO synthesis. In the first step, the stoichiometric amount of Zinc acetate dehydrates was dissolved in deionized water under continuous stirring at 1200 rpm on a magnetic stirrer at room temperature to establish a homogenous solu­ tion. In the next step, the prepared acidic solution of Zn+2 ions was neutralized using NaOH solution. The addition of NaOH solution drop­ wise in zinc acetate dehydrate solution results in white color pre­ cipitates, which was filtered out to get final ZnO nanoparticles. The residue was dried at 60 ◦ C in an air oven for 2 hours, followed by calcination at 300 ◦ C in a muffle furnace for 3 hours. The prepared sample was crushed to get fine ZnO nanoparticles. The steps involved in the synthesis process have been shown in Fig. 1. Further, the synthesized ZnO nanoparticles were blended with the PVDF matrix twin screw compounder. The linseed oil has used for particle dispersion with PVDF granules. The chemical reactions involved in the synthesis of ZnO nano­ particles by the co-precipitation process have given below [43]. Zn(CH3COO)2.2H2O + 2 NaOH → Zn(OH)2+2CH3COONa +2H2O Zn(OH)2 (gel) + 2H2O = Zn

2+

+ 2OH- + 2H2O = [Zn(OH)4]

2

(2) (3)

ZnO2-2 +2H2 O ↔ZnO + 2OH−

(4)

-2

Exp no.

ZnO concentration

Forced loading

Torque

1 2 3 4 5 6 7 8 9

0% 0% 0% 1% 1% 1% 2% 2% 2%

10.0 kg 12.5 kg 15.0 kg 10.0 kg 12.5 kg 15.0 kg 10.0 kg 12.5 kg 15.0 kg

0.10 N.m 0.15 N.m 0.20 N.m 0.15 N.m 0.20 N.m 0.10 N.m 0.20 N.m 0.10 N.m 0.15 N.m

conducted under this condition to ensure the inducement of crystallinity in the ZnO nanoparticles after sintering. Further, in a similar inert condition, DSC of virgin PVDF, PVDF + 1%ZnO and PVDF + 2%ZnO have been performed under 30-230 ◦ C. It should be noted that the two continuous cycles of endothermic and exothermic reactions have been conducted to check the thermal stability in the prepared sample. The feedstock filaments of PVDF reinforced with ZnO particles were prepared on twin screw compounder (Make: Thermo Fisher Scientific, maximum temperature: 300 ◦ C, maximum screw speed: 300 rpm) at 190℃ barrel temperature. The twin screw compounder was used to prepare the continuous feedstock filaments. The aim of using the twin screw extruder includes; the mixing/blending of two or more than two materials is very easy and provides the homogeneity in particle disper­ sion. The problems that occurred in the single screw extrusion, like the formation of tiny pores, blowholes, and incomplete blending, can be encountered by using the twin screw compounder. The twin screw compounder provides better control over the blending, shearing, cooling rate, compression, transporting filaments, and shaping [44]. The control of applied load and torque is an important aspect of the properties of extruded filaments. The previous study suggested that altering the torque has a significant impact on the fluctuation of extruding flow. If torque increases, then it is obvious that screw speed will increase. Due to that flow of materials through the nozzle will fluctuate, and this can result in the inaccurate dimension, and incor­ poration of void and surface defects lead to reduced mechanical prop­ erties [45]. On the other hand, altering the forced loading can result in the torsion of the feedstock filaments. The accurate 3D printing required the torsion-free feedstock filaments. The filaments were processed under different processing conditions as per Taguchi L9 orthogonal array considering 3 levels of ZnO nano-particles concentration (0%, 1% and 2%), forced loading (10 kg, 12.5 kg and 15 kg) and torque (0.10 N.m, 0.15 N.m and 0.20 N.m). The levels of ZnO concentration, forced loading and torque have been selected as per the uniformity of the feedstock filament (1.75 ± 0.10 mm). The pilot experimentation has been conducted to discard the other levels of selected where uniformity of the feedstock was not achieved. Table 1 shows the experimental design for filament preparations. To investigate the impact of process variables on the mechanical properties, the tensile tests of cylindrical feedstock filaments (universal

(1)

[Zn(OH)4] –↔ ZnO2 +2H2O

2

Table 1 Experimental design for filament preparation

The first equation (i) indicates mixing acidic ZnO solution and basic NaOH solution to start the precipitation process. The second (ii) and third reaction (iii) is an intermediate state during the formation of the final product. The precipitation process yield Zn(OH)2, which further react with H2O and form another intermediate state [Zn(OH)4]2 and ZnO-2 2 , The equation four (iv) shows formation the ZnO nanoparticles. Experimentations The analytical testing approaches like; differential scanning calori­ metric (DSC), universal tensile testing, Shore D hardness, scanning electron microscope (SEM), energy dispersive spectroscopy (EDX) and shape memory investigations have been performed. The DSC analysis (Make: Mettler Toledo, Maximum temperature: 600 ◦ C) has been conducted for ZnO nanoparticles (before and after sintering) at 25-500℃ under continuous flow of N2 (N2 has been selected for inertness) gas of 50 ml/min. On controlling the heating rate of +10℃/min for endothermic reaction and cooling rate of -10℃/min for an exothermic reaction, the DSC test was performed. The test

Fig. 2. Schematic of tensile testing and dimension of the specimen. 270

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Fig. 3. DSC analysis of virgin PVDF and extruded PVDF.

Fig. 4. DSC analysis of PVDF-1%ZnO and PVDF-2%ZnO.

tensile tester (make: Shanta engineering (India), capacity: 5000 N)) have been conducted by maintaining the 50 mm grip separation and 50 mm/min strain rate. The results of the tests observed in the form of ultimate tensile strength, fracture tensile strength, percentage

elongation at the peak, and percentage elongation at break. There are 3 number of repetitions of the tensile test been performed to reduce the measurement errors. Fig. 2 shows schematics of tensile testing and dimension of specimen.

Table 2 Observations for mechanical properties Experiment no.

ultimate tensile strength (MPa)

Fracture tensile strength (MPa)

Percentage elongation at peak (%)

Percentage elongation at break (%)

Young’s Modulus (MPa)

Modulus of toughness (MPa)

Shore D hardness (Shore D)

1 2 3 4 5 6 7 8 9

47.7 ± 0.45 44.15 ± 0.19 48.58 ± 0.47 37.23 ± 0.12 35.06 ± 0.47 37.4 ± 0.48 40.64 ± 0.53 35.79 ± 0.86 41.03 ± 0.85

42.93 ± 0.54 39.74 ± 0.21 43.72 ± 0.42 33.5 ± 0.11 31.55 ± 0.50 33.66 ± 0.52 36.57 ± 0.49 32.21 ± 0.90 36.93 ± 0.82

11 ± 1.0 7 ± 1.0 16 ± 2.0 9 ± 1.0 12 ± 1.0 8 ± 1.0 8 ± 1.0 7 ± 1.0 12 ± 1.0

12 ± 1.0 9 ± 1.0 31 ± 3.0 9 ± 1.0 13 ± 1.0 10 ± 1.0 9 ± 1.0 8 ± 1.0 15 ± 1.0

418.42 ± 5.50 663.90 ± 2.33 295.02 ± 4.35 419.88 ± 1.51 299.23 ± 5.65 472.42 ± 4.82 513.34 ± 4.26 491.39 ± 9.51 332.22 ± 8.98

2.65 ± 0.08 1.76 + 0.05 6.71 ± 0.42 1.53 ± 0.09 2.04 ± 0.08 1.70 ± 0.07 1.62 ± 0.06 1.27 ± 0.04 2.80 ± 0.06

50.0 ± 1.0 46.0 ± 1.0 52.5 ± 1.0 36.0 ± 1.5 35.0 ± 1.0 39.0 ± 1.0 34.0 ± 0.5 25.0 ± 1.5 40.0 ± 1.0

271

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Fig. 5. Stress versus strain curves for fractured feedstock filaments.

Fig. 6. Photomicrographs of fractured feedstock filaments.

272

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Fig. 7. SEM observations of sample 3, sample 5 and sample 9 at ×50, × 200, × 800 magnifications.

SEM analysis of (Make: Jeol, Model: JSM-IT500) on the cross-section has been performed to investigate the fracture morphology. The frac­ tured samples obtained after tensile testing were carefully cut into 10 mm length without touching the surface fracture region. The samples were then placed to SEM mount for further investigations. The SEM images have been taken at ×50, ×200, and ×800 magnifications under 15 V power supply on vacuum mode: high vacuum. In addition, EDX analysis has been carried to investigate the atomic and mass fraction of the elements on the fractured region (cross-section of feedstock filament). The surface profilometry of the fractured part were measured by using the Gwyddion 2.55 software package with SEM photomicrography profile. The surface profilometry has been conducted by keeping the cutoff length of 0.04 mm across the fractured region of the feedstock fila­ ments. The full width at half maximum (FWHM) of the Gaussian has been used as the smoothening filter. The filter has been used on the entire 2D fracture profile for surface roughness measurement. The filter setting for FWHM of Gaussian can be expressed as [46]; FWHM = 2√(2ln2)σ = 2.3548σ

portable Shore D hardness tester. The Shore D hardness was measured by the indenting with the stylus by pressing the punching knob of the Shore D hardness tester. It should be noted that the knob has punched and kept in a static position until the result on display is stable. The observations were taken in 3 repetitions to minimize the measurement errors. The surface of the fractured filament has been characterized as the results of percentage porosity. The approach is used in the optical mi­ croscopy is based upon the intransitive light emission directed to the surface of the filament cross-section. The metallurgical image analysis software (MIAS) can predict the pores of 2D cross-section into 3D structures. The error of measurement in the case of optical microscopy is common since it is hard to differentiate between the close pore and open pores. But the use of MIAS can provide a strong indication for the sig­ nificance of close pores and open pores. Using the MIAS, it has a mini­ mum chance of error in porosity measurement [47–48]. The measurement of porosity has been repeated three times for each experiment to reduce the experiment errors. The presence of porosity in the case of the present study is the entrapment of the gas permeability due to processing variables. It should be noted that gas permeability entrapped with the filaments is generally varied with changing the process variable, ZnO loading forced to load and torque. The 4D capabilities are referred to the special function of the mate­ rials in the form of the shape-changing abilities. For example, if a structure changes its original shape and either contract or expand after application of external stimuli such as; heat, force, pH, humidity, elec­ tric current, magnetic field etc., and when the structure reaches near to

(5)

Where σ is the standard deviation of the distribution, the mean of distribution has assumed 0. Further to investigate the effect of ZnO concentration, Shore D hardness (Make: Bloomerang) measured. A special clamping has been prepared by 3D printing to hold the feedstock filaments for hardness testing. The hardness was measured as per the ASTM D2240 standard by 273

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Fig. 8. EDX of sample 3, sample 5 and sample 9.

the original shape fully of practically after the release of such stimuli then this structure can be termed as 4D capable structure. The 3D printing of 4D capable materials is called 4D printing [28–30]. To investigate the 4D capability of the prepared materials, samples were exposed to different stimuli for 5 hrs. The shape memory effect of the prepared samples was calculated by keeping the samples in water at and 25 ◦ C and 40 ◦ C. The immersion of the sample has been kept in water for 5 hours, and then dimension and weight measured. Further, the samples have been kept in room drying for the next 5 hours, and then dimension and weight have been measured. The shape memory effect has been investigated by observing the recovery in weight and dimensions.

nanoparticles, which are sintered have released some amount of heat during endothermic reaction shows the sign for crystallinity. Effect of melt processing on thermal properties of PVDF Further thermal analysis of virgin PVDF and extruded PVDF has been conducted to investigate the effects of melt processing. It has been observed that both samples were responsive to the endothermic heat reaction, which is led to depression in melting points. As observed, the melting of virgin PVDF is shifted from 177.32℃ in 1st cycle to 175.50℃ in 2nd cycle. The melting point of extruded PVDF is shifted from 176.37℃ in 1st cycle to 173.34℃ in 2nd cycle (see Fig. 3). Although the depression in the melting point is observed in the minor range of ~2℃, this depression range will be very crucial for sensor applications. The most interesting fact is observed in the relation of normalized heat capacity of the materials where endothermic reaction led to better stabilization. The normalized heat capacity of virgin PVDF is shifted from -25.16 J/g in 1st cycle to -27.78 J/g in 2nd cycle and normalized heat capacity of extruded PVDF is shifted from -28.01 J/g to -34.39 J/g. This means, with every cyclic exposure of heat to the PVDF, elevated their normalized heat capacity, which shows the sign of good thermal responsive behavior. On the other hand, there is no major impact of exothermic reaction observed in the case of solidification of the samples.

Results and Discussions Thermal analysis Crystallinity check in ZnO nanoparticles The presence of crystallinity is ascertained by exposure of ZnO nanoparticles to the regular endothermic and exothermic reactions. It has been noticed that ZnO nanoparticles before sintering has taken a continuous amount of heat during endothermic thermal reaction show that there is no crystallinity present. But in the case of ZnO 274

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Fig. 9. 3D rendered profile and Ra profile of (a) sample 3, (b) sample 5 and (c) sample 9.

The solidification point of both (virgin PVDF and extruded PVDF) samples were gradually elevated by heating exposure. It has been noticed that solidification point of virgin PVDF is elevated from 136.92℃ in 1st cycle to 137.11℃ in 2nd cycle and a solidification point of extruded PVDF is elevated from 135.78℃ in 1st cycle to 135.81℃ in 2nd cycle. Similarly, their low changes in the normalized heat capacity of virgin and extruded PVDF is observed, for virgin PVDF it has been reduced from 45.90 J/g in 1st cycle to 44.18 J/g in 2nd cycle whereas for extruded PVDF it has reduced from 42.52 J/g in 1st cycle to 41.43 J/g in 2nd cycle. The result of the melting and solidification points confirms that PVDF is an excellent material with thermal responsive character­ istics that may be used for 3D printing in sensor applications.

thermally tested to analyze thermal behavior (see Fig. 4). In the endo­ thermic reaction, the normalized heat capacity of PVDF-1%ZnO is shifted from 33.07 J/g in the first cycle to -39.19 J/g in 2nd cycle, and for PVDF-1%ZnO it is shifted from -31.23 J/g in 1st cycle to -38.31 J/g. It should be noted that there is no significant difference of the normal­ ized heat capacity in between PVDF-1%ZnO and PVDF-2%ZnO, but the tendency to gain the normalized heat capacity during endothermic re­ action for both compositions is found similar. Since the normalized heat capacity is elevated for both the composition so that these are thermally responsive to the external environment so these are good as sensor materials and may be used for the 3D printing of biosensor and biomedical sensors. The effect of ZnO in the PVDF has also been observed in terms of melting points. The depression in melting point during cycling endothermic reaction of PVDF-1%ZnO (depressed from 176.75℃ in 1st cycle to 173.88℃ in 2nd cycle, depression: 2.87℃) is found more as compared to PVDF-2%ZnO (depressed from 175.31℃ in 1st cycle to 173.12℃ in 2nd cycle, depression: 2.19℃). This may be due

Effect of ZnO concentration as reinforcement in PVDF matrix The reinforcement of ZnO in the PVDF matrix is the concern of enhancing the material responses for applications in biosensors/ biomedical sensors. The PVDF reinforced with 1% ZnO, and 2% ZnO is 275

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Table 3 SN ratios for mechanical and morphological properties Experiment no.

SN ratio of ultimate tensile strength (dB)

SN ratio of fracture tensile strength (dB)

SN ratio of percentage elongation at peak (dB)

SN ratio of percentage elongation at break (dB)

SN ratio of Shore D hardness (dB)

Normalized SN ratio of mechanical properties (dB)

SN ratio of Young’s Modulus (dB)

SN ratio of modulus of toughness (dB)

SN ratio of percentage porosity (dB)

1 2 3 4 5 6 7 8 9

33.57 32.89 33.72 31.41 30.89 31.45 32.17 31.07 32.26

32.65 31.98 32.81 30.50 29.98 30.54 31.26 30.15 31.34

20.82 16.90 24.08 19.08 21.58 18.06 18.06 16.90 21.58

21.58 19.08 29.82 19.08 22.27 20.00 19.08 18.06 23.58

33.97 33.25 34.40 31.12 30.88 31.82 30.62 27.95 32.04

28.45 27.42 29.58 27.63 28.31 27.62 27.51 26.99 28.59

52.43 56.44 49.39 52.46 49.52 53.48 54.20 53.82 50.42

8.46 4.91 16.54 3.69 6.19 4.60 4.19 2.07 8.94

− − − − − − − − −

to the fact that PVDF-2%ZnO is thermally more stable material as compared to PVDF-1%ZnO. As observed from the exothermic reaction, there are no significant differences in the solidification points of PVDF1%ZnO, and PVDF-2%ZnO was observed in 1st cycle and 2nd cycle. The solidification point of PVDF-1%ZnO is maintained 137.86℃ in 1st cycle to 137.88℃ in 2nd cycle, and for PVDF-2%Zno, it was maintained 137.52℃ in 1st cycle to 137.53 in 2nd cycle. It means there is no impact on the solidification of the samples either by varying proportions of ZnO nanoparticles or passing through exothermic reactions. Although the materials are stable in the exothermic phase at the same instance may not be applied for the preparation of sensors for exothermic conditions. Also, the normalized heat capacity of both materials is stable in the exothermic reaction. The normalized heat capacity of PVDF-1%ZnO is maintained 46.01 J/g in 1st cycle to 45.22 J/g, and for PVDF-2%ZnO it was maintained from 46.05 J/g in 1st cycle to 45.60 J/g in 2nd cycle.C

22.99 22.62 18.01 34.18 34.21 34.86 31.66 29.51 27.08

(in 1% ZnO loading group) and experiment no. 9 (in 2% ZnO loading group) where high forces lead to high tensile strength. Fig. 5 shows the stress vs. strain curves of feedstock filaments sub­ jected to tensile fracture. The area under the curve is considered as modulus of toughness. The modulus of toughness plays an important role in the manufacturing railroad couplers (made of highly ductile steel) where it preferred high modulus of toughness [49]. The present materials combination can be useful for the preparation of hook type sensors of polymeric materials. In a practical situation, the structures with a high modulus of toughness would withstand the impact load. As per the application viewpoint, it is desirable that the area under the curve is maximum, and that can be obtained by both maintaining stress and strain. In the present case, the area under the curve is obtained maximum for the filament of experiment no. 3 (6.71 MPa at 0% ZnO), minimum for sample 8 (1.27 MPa at 2% ZnO) maximum with ZnO concentration at experiment no. 9 (2.80 MPa at 2% ZnO). So, based upon these results, the sample 9 is better for manufacturing of sensors. On the other hand, the elongation in the feedstock filament largely affected their Young,s modulus, due to less elongation in sample 2, Young’s modulus obtained was maximum (663.90 MPa) and due to high elongation in sample 3, the minimum Young’s modulus is obtained (295.02 MPa).

Mechanical properties of feedstock filaments Table 2 shows the observed values of tensile properties of fractured filaments in terms of ultimate tensile strength, fracture tensile strength, percentage elongation at the peak, percentage elongation at break, young’s modulus, modulus of toughness. It has been observed that the maximum ultimate tensile strength (48.58 MPa) and maximum fracture tensile strength (43.72 MPa) results in experiment no. 3, which is without ZnO reinforcement and extruded at 15 kg forced loading and 0.2 Nm torque. On the other hand, the filament extruded at of experi­ ment no. 5 (with PVDF-1 wt.%ZnO powder, extruded at 0.2 Nm torque and 12.5 kg loading) gives minimum ultimate tensile strength (35.06 MPa) and minimum fracture tensile strength (31.55 MPa).The aim of this study to get the maximum strength with some ZnO concen­ tration, it has been observed that sample of experiment no. 9 which is extruded at 0.15 Nm torque and 15 kg forced load gives maximum ul­ timate tensile strength (41.03 MPa) and maximum fracture tensile strength (36.93 MPa) and maximum toughness (2.80 MPa). Considering the PVDF reinforced with ZnO loading, the minimum strength at experiment no. 5 and maximum strength at experiment no. 9, may have resulted due to the fact that an insignificant amount of ZnO (1%) has just created the spaces between the plastic matrix, which further led to the formation of voids in extruded filaments. On the other aspect, the 2% ZnO concentration in the PVDF matrix led to the formation of proper PVDF-ZnO blend with minimum porosity so that tensile strength resul­ ted in maximum. The maximum Shore D hardness has observed for sample 3 (52.5 shore D), extruded at 0.2 Nm torque, and 15 kg forced loading. In the set of filaments ZnO reinforcement sample 9 extruded at 0.15 Nm torque, and 15 kg forced load gives maximum shore hardness (40 Shore D). It has been observed form the micrographic observation that cross-section of the sample obtained at experiment no. 9 has appeared with stretched surface and with lesser evidence of voids due to high forced loading and intermediate torque (see Fig. 7). It has been observed that high forced loading provides the strain hardening of the extruded filament. It can also be co-verified with the experiment no. 6

Morphology of fractured surface Fig. 6 shows the cross-sectional view of photo-micrographs for fractured filaments. The study has conducted to observe the effect of the input process variable on surface defects/voids at ×30 magnification. It has been mentioned by mechanical testing that sample 3 results in better mechanical strength and sample 5 into poor mechanical strength. The results of the porosity on the fractured surface show that samples (PVDF0%ZnO) have obtained minimum porosity (7.96%), which is extruded at 15 kg applied load and 0.2 Nm torque. This may be due to the fact that no ZnO concentration and high forced load in filament preparation has led to compact the polymer film so that maximum strength and mini­ mum defects were exhibited. In the 5 samples with PVDF- 1% ZnO, extruded at 0.2 Nm torque and 12.5 kg load gives minimum strength at because of maximum porosity (51.37%). As per ZnO concentration in the PVDF matrix, the maximum strength was obtained for sample 9, the porosity observed in the sample 9 is 22.61% (minimum in ZnO rein­ forced filaments) To cross verify the results of the mechanical and morphological testing, the fracture region of a cross-section of sample 3, sample 5, and sample 9 was captured via SEM at different magnifications i.e 50X, 200X, 800X (see Fig. 7). The most critical observation may be seen from the particular fracture fashion of the feedstock filaments. The sample 3 (with maximum strength) has been fractured with the sigh of ductile fractures, the elongated portion at the cross-section shows the well established ductile fracture (the percentage elongation at break in sample 3 is maximum: 31%). The cross-section of the fractured filament of sample 5 has a sign of brittle fracture, as this is observed with no 276

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Fig. 10. SN ratio plot for (a) Ultimate tensile strength, (b) Fracture tensile strength, (c) Percentage peak elongation, (d) Percentage break elongation, (e) Young’s Modulus, (f) Modulus of toughness, (g) Shore D hardness and (h) Percentage porosity.

277

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Table 4 ANOVA of SN ratios of ultimate tensile strength. Source

DF

Seq SS

Adj SS

Adj MS

F

P

Percentage contribution (%)

ZnO concentration (%) Forced load (Kg) torque (Nm) Residual Error Total

2 2 2 2 8

7.36 1.33 0.08 0.04 8.82

7.36 1.33 0.085 0.043

3.68 0.66 0.042 0.021

169.14 30.64 1.96

0.006 0.032 0.33

83.44 15.07 0.907 0.45

DF-degree of freedom, Seq. SS- sum of squares, Adj SS- Adjusted sum of squares, Adj MS- Adjusted mean of squares, F-Fisher’s value, P- probability.

experimental trials. A mathematical optimization based approach has been used to evaluate the optimum set of process parameters. As per values given in Table 2, The SN ratio (Ƞ) for ‘maximum is better’ type case has been calculated for the optimization of input pro­ cess variables [50]: [ / ] n ∑ 1 (6) η = − 10 1 n y2 k=1

Table 5 Response Table for SN ratios of ultimate tensile strength Level

ZnO concentration (%)

Forced load (Kg)

Torque (Nm)

1 2 3 Delta Rank

33.40 31.26 31.84 2.14 1

32.39 31.62 32.48 0.86 2

32.03 32.19 32.27 0.23 3

Where n is the no. of the experiment, and y is the material properties at experiment no. k. Table 3 shows the SN ratios for mechanical and morphological properties. Fig. 10 shows the plot for SN ratios of each mechanical and morphological property. It is suggested by main effects plots that ulti­ mate tensile strength, fracture tensile strength, percentage elongation at the peak, percentage elongation at break and modulus of toughness will be maximum if the experiment is conducted by 0%concentration of ZnO in PVDF at 15 kg forced to load and 0.20 N.m torque (experimental condition is same as experiment no. 3). The utility of ZnO reinforcement is justified by the fact that maximum Young’s modulus will be achieved by maximum ZnO concentration (PVDF-2%ZnO) with 15Kg forced to load and 0.1 N.m torque. To calculate the optimum quantity of mechanical strength at the predicted setting, the analysis of variance (ANOVA) conducted for various SN ratios of tensile test results. Table 4 shows the ANOVA of SN ratios for the ultimate tensile strength of feedstock filaments. It has been observed that varying ZnO concentration on the PVDF matrix has maximum impact and contributed for 83.44%, followed by the forced load (15.07%) and torque (0.907%). The residual error obtained for this process is 0.45% which is lesser than 5% so that this process is theo­ retically accepted for production purpose. This is also cross verified by the results of P-value, as the P-value for ZnO concentration obtained is 0.006% (significant if P ≤ 0.05) and for the forced load (0.032). Table 5 shows the response of the SN ratios for varying the input process variable for feedstock filament preparations. It has been observed that delta value (maximum value of SN ratio-minimum value of SN ratio) is observed maximum for ZnO concentration (2.14 dB) followed by the forced load (0.86 dB) and torque (0.23 dB) so that ZnO loading has ranked 1, forced load is 2 and torque is ranked 3. As per the process, the parametric setting predicted in Fig. 10, the predicted value of mechanical and morphological properties have been calculated for parametric optimization. It has been predicted that ulti­ mate tensile strength can be optimum if the process is being conducted by taking 0% ZnO, 15 kg forced loading, and 0.2 N.m torque. The properties at the predicted setting can be calculated by defining the optimum SN ratio (Zopt) [50]:

elongated pores on the cross-section. In sample 5, there is a large porous defect observed, which caused by minimized the mechanical strength of the filament. The sample 9 (maximum tensile strength with ZnO con­ centration) has been fractured with both brittle and ductile way; some region of the fractured part is obtained with ductile fracture and some with brittle fracture. This observation is also in line with the maximum percentage elongation at break (15%) for ZnO concentrated samples. The EDX analysis has been conducted to investigate the atomic and mass fraction of the PVDF, PVDF-1%ZnO and PVDF-2%ZnO filaments. It should be noted that sample 5 (PVDF-1%ZnO) has a maximum mass fraction of carbon (C) (32.65 ± 0.74%) in the compound and PVDF-0% ZnO has a minimum mass fraction of C (32.67 ± 53%) in the compound (see Fig. 8). It is well known that increasing the carbon content in any compound reduced their ductility and encouraged the brittleness. In the present case, due to an un-optimized parametric combination in sample 5, the emission (due to fumes of extrusion) of carbon may be increased and associated with the PVDF-1%ZnO samples. So, due to the increased mass fraction of C in the sample 5 led to brittle fracture and tensile strength has been reduced. On the other hand, the mass fraction of C in sample 9 has been observed (30.85 ± 42%) in between the C content of sample 5 and sample 3, so that the fracture fashion of sample 9 has been obtained with a combination of brittle and ductile fracture. The presence of the Zn in the samples has also been detected by EDX analysis, and it is observed that the mass fraction of Zn in sample 9 (0.00 ± 0.78%) is more as compared to sample 5 (0.00 ± 0.56%). Surface topology of the fractured surface The SEM images of the cross-section have been rendered to observe the failure mechanism. In addition, the surface roughness of the crosssection has been observed. As observed in Fig. 9, the pores originated from the surface are found maximum in sample 3 followed by sample 9 and sample 5. The brittle fracture in sample 5 is also verified by the minimum surface roughness value (38.11 μm), which is obtained less as compared to sample 3 (48.14 μm) and sample 9 (57.13 μm). The surface roughness is obtained maximum in case of sample 9 due to the fact that the presence of combined brittle and ductile fracture resulted in more peak and valleys on the cross-section. In addition, the Rz (average maximum height of profile) and Ry (maximum peak to valley roughness) have measured. The result of Rz and Ry are in-line with the Ra.

Zopt = k+(kA1–k) +(kB3–k) +(kC3–k)

(7)

Where k is the average of SN ratio on 9 different experiment settings, kA1 is the ZnO concentration of level 1 (0%), kB3 is the forced load of level 3 (15 kg), and kC3 is the torque of level 3 (0.20 N.m). The optimum properties can be calculated by giving the value of Zopt in equation (vii) [50]:

Optimization of process variables and confirmatory experiments The Taguchi L9 orthogonal array selected a total number of 9 ex­ periments out of the 27 original experiments to reduce large

Qopt2 = (10)Zopt/10 ……………… (viii), for, larger is better type properties

278

43.70

31

48.60

13

Predicted value Actual value at predicted setting

0% ZnO concentration, 15Kg applied load, 0.2 N.m torque 27.86

0% ZnO concentration, 15Kg applied load, 0.2 N.m torque 10.71

0% ZnO concentration, 15Kg applied load, 0.2 N.m torque 44.25

0% ZnO concentration, 15Kg applied load, 0.2 N.m torque 49.07

Predicted setting

Percentage elongation at break (%)

Percentage elongation at peak (%)

Fracture tensile strength (MPa)

Ultimate tensile strength (MPa)

Properties

Table 6 Predicted and observed values of mechanical and morphological properties

6.15

0% ZnO concentration, 15Kg applied load, 0.2 N.m torque 6.11

Modulus of toughness (MPa)

528.0

2% ZnO concentration, 12.5Kg applied load, 0.1 N.m torque 524.20

Young’s modulus (MPa)

60.0

0% ZnO concentration, 15Kg applied load, 0.15 N.m torque 57.54

Shore D hardness (Shore D)

0.20

0% ZnO concentration, 15Kg applied load, 0.15 N.m torque 0.11

Percentage porosity at fracture region (%)

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Fig. 11. Contour plots for the mean of SN ratio vs. (a) ZnO concentration and applied load, (b) applied load and torque, and (c) torque and ZnO concentration.

Qopt2 = (1/10)Zopt/10 ………….…….(ix), for, smaller is better type properties

Where Qopt is the ultimate tensile strength at predicted setting The average value of SN ratios (k) for ultimate tensile strength has been calculated as; k = 32.166 dB As per the results of Table 5, kA1 = 33.40 dB, kB3 = 32.48 dB, kC3 = 32.27 dB Now from equation (i) Zopt = 32.166+(33.40-32.166) +(32.48-32.166) +(32.27-32.166) Zopt = 33.81

279

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

mechanical properties will be obtained with 0%ZnO concentration under 0.19-0.20 N.m torque. Overall, it has been observed that 0%ZnO concentration, 14.5-15 kg forced to load, and 0.19-0.20 N.m torque is desirable for obtaining maximum mechanical strength.

Table 7 Shape changes and wettability of samples at 25℃ Specimen

Initial Length (mm)

PVDF 0% 10.05 PVDF 1% 10.05 PVDF 2% 10.04 After 5 hrs. immersion to PVDF 0% 10.10 PVDF 1% 10.09 PVDF 2% 10.09 After 5 hrs. Exposure PVDF 0% 10.03 PVDF 1% 10.03 PVDF 2% 10.02

Minor Dia. (mm)

Major Dia. (mm)

Volume

π

= ×length × major 4 dia. ×minor dia. (mm3)

Weight (g)

1.69 1.86 1.63 1.73 1.09 1.23 water stimuli at 25 ◦ C 1.70 1.99 1.63 1.73 1.08 1.18

24.7991 22.2469 10.5666

0.0486 0.0387 0.0197

26.8221 22.3354 10.0941

0.0481 0.0386 0.0198

1.70 1.53 1.10

26.6362 20.7200 10.3827

0.0478 0.0385 0.0197

1.99 1.72 1.20

Shape memory effect Dimensional changes and wettability The dimensional changes (length and diameter of feedstock fila­ ments) in the sample of PVDF and PVDF-1%ZnO have been increased significantly after 5 hrs immersion to the water at 25℃ (see Table 7). But surprisingly, the diameter of the PVDF-2%ZnO sample has reduced. Again after 5 hrs drying at 25℃, all samples have memorized their shape and returned to the initial shape. Wettability is the major concern of any biosensors/biomedical sensors which is need to be minimized. The general polymers are hydrophobic in nature and absorb moisture when they come in contact with water. In the present case, the sample prep­ aration is stable, and there is no effect of water has been observed. Since the sample resist water perfectly so that there is an eligible candidate for the 3D printing of biosensors/biomedical sensors. The normal body temperature is near about 40℃ (considering 3-4℃ heat loss) and the shape memory effect is also observed at this tem­ perature (see Table 8). It has been observed with more surprisingly, that 5 immersions in the water at 40℃ have decreased the initial length and diameter of the samples. Further, the drying at 25℃ room temperature have memorized their shape and observed dimensions more than their initial dimensions. In this case, there is no effect on the moisture absorbance is observed so that these materials are not even hydrophobic at 40℃ water temperature. So these materials are having good shape memory effect and resistibility to water intake. Finally, the shape memory recovery of each sample has been calcu­ lated for both water stimulus (water at 25℃ and water at 40℃). It has been observed that at 25℃ conditions, the minimum shape recovery is obtained for PVDF-0%ZnO samples (92.6%) and maximum shape re­ covery for PVDF-2%ZnO (98.22%). For 40℃ water stimulus, the maximum shape recovery is obtained for PVDF-1%ZnO (93.1%) and maximum for PVDF-0%ZnO (99.58%) (see Table 9) since the shape changes are observed significantly in all the samples so that these composite materials are good to apply in actuators and customized ac­ tuators can be 3D printed.

Table 8 Shape changes and wetability of samples at 25℃ Specimen

Initial Length (mm)

Minor Dia. (mm)

Major Dia. (mm)

PVDF 0% 10.00 1.70 1.94 PVDF 1% 10.00 1.48 1.70 PVDF 2% 10.02 1.08 1.24 After 5 hrs. immersion to water stimuli at 40 ◦ C PVDF 0% 9.96 1.66 1.85 PVDF 1% 9.98 1.46 1.66 PVDF 2% 9.77 1.10 1.18 After 5 hrs. exposure to air PVDF 0% 10.05 1.71 1.91 PVDF 1% 10.03 1.45 1.61 PVDF 2% 10.08 1.10 1.19

Volume

π

= ×length × major 4 dia. ×minor dia. (mm3)

Weight (g)

25.8893 19.7506 10.5337

0.0488 0.0352 0.0195

24.0109 18.9872 9.9549

0.0489 0.0351 0.0192

25.7671 18.3808 10.3579

0.0489 0.0352 0.0192

Putting the values of Zopt in equation (vi1i) Qopt2 = (10)Zopt / 10 Qopt2 = (10)33.81/ 10 Qopt =49.07 MPa At the predicted setting for ultimate tensile strength, the predicted ultimate tensile strength is calculated as 49.07 MPa. Again, on the predicted setting process have been repeated, and the feedstock fila­ ments have been prepared, and the actual values of each property have been obtained. Table 6 shows the predicted and observed values of each property.

Printability of the PVDF-2%ZnO composites It has been critically verified by thermal, mechanical, and shape memory analysis that PVDF-2%ZnO is an excellent material for actua­ tors/biosensor/biomedical sensors. The printability of the samples has checked by 3D printing on a commercial FDM setup (Make: divide by zero). The 3D printing by FDM has been performed at 200 ◦ C bed tem­ perature, 60 ◦ C bed temperature, 100% infill density, 0.4 mm nozzle diameter, 60 mm/s printing speed, 0.1 mm layer height, and rectilinear fill pattern. It has been observed that the 3D printed part of PVDF-2% ZnO is dimensionally correct, and no more than ±3% volumetric vari­ ations have been observed (see Fig. 12). So, this combination of material

Combined optimization of process variables Fig. 11 shows the contour plots of the mean of SN ratios. In this study, the interaction between all process variables have been made, and the optimum range of process variable has been select a single setting to maximized the mechanical properties. Fig. 11(a) shows that the me­ chanical properties will be obtained maximum when the forced load is maintained between 14.5-15.0 kg at 0%ZnO concentration in PVDF. Fig. 11(b) shows that a forced load of 14.0-15.0 kg with 0.18-0.20 N.m will give mechanical strength. Fig. 11(c) shows that the maximum Table 9 Shape memory effect of PVDF, PVDF-1% ZnO and PVDF-2% ZnO samples Materials (Shape memory effect)

Under room temperature (25 ◦ C)

Under stimulated environment (40 ◦ C)

% change in volume after 5 hrs. immersion

% change in volume after 5 hrs. at atmospheric temperature

Recovery percentage by volume after 5 hrs exposure to room temperature

% change in volume after 5 hrs. immersion

% change in volume after 5 hrs. at atmospheric temperature

Recovery percentage by volume after 5 hrs exposure to room temperature

PVDF PVDF-1% ZnO PVDF-2% ZnO

8.15 0.39

7.4 − 6.8

92.6 % 93.2 %

− 7.2 − 3.86

− 0.42 − 6.9

99.58 93.1

− 4.47

− 1.78

98.22 %

− 5.4

− 1.66

98.34

280

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282

Fig. 12. 3D printed part of PVDF-2%ZnO its dimension (all dimensions in mm).

is good for 3D printing of actuator/biosensors/biomedical sensors.

• The printability check is found feasible with ±3% volumetric vari­ ations for PVDF-2%ZnO, which shows control over printability for actuators/biosensors/biomedical sensors.

Conclusions

This study is limited to pure ZnO reinforcement in the PVDF ther­ moplastic matrix for the investigation of mechanical, thermal, morphological, and shape memory effects. It has been established that doping of rare earth metals and transition metals excellently tunes the optical, photoluminescence, piezoelectricity, magnetic properties, electrical and thermal conductivity of ZnO nanoparticles. Future studies may be extended with reinforcement of rare earth and transition metals doped ZnO in PVDF for the preparation of protein, dopamine, and glucose-based biosensors.

Following conclusions have been drawn from the present study: • The endothermic heat reaction has led to depression in melting points of virgin PVDF, PVDF-1%ZnO and PVDF-2%ZnO. Although the depression in the melting point is observed in the minor range of ~2℃, this depression range is very crucial for sensor applications. The depression in melting point during cycling endothermic reaction of PVDF-1%ZnO (depressed from 176.75℃ in 1st cycle to 173.88℃ in 2nd cycle, depression: 2.87℃) is found more as compared to PVDF2%ZnO (depressed from 175.31℃ in 1st cycle to 173.12℃ in 2nd cycle, depression: 2.19℃). This may be due to the fact that PVDF-2% ZnO is thermally more stable material as compared to PVDF-1%ZnO. • I was considering the PVDF reinforced with ZnO concentration, the minimum strength at experiment no. 5 (PVDF-1%ZnO) and maximum strength at experiment no. 9 (PVDF-2%ZnO), may have resulted due to the fact that insignificant amount of ZnO (1%) have just created the spaces between the plastic matrix which further led to the formation of voids in extruded filaments. On the other aspect, the 2%ZnO concentration in the PVDF matrix led to the formation of proper PVDF-ZnO blend with minimum porosity so that tensile strength resulted in maximum. Overall, it has been ascertained that 0%ZnO concentration, 14.5-15 kg forced loading, and 0.19-0.20 N. m torque is desirable for obtaining maximum mechanical strength. • The surface roughness is obtained maximum in the case of sample 9 due to the fact that the presence of combined brittle and ductile fracture resulted in more peak and valleys on the cross-section. • The general polymers are hydrophobic in nature and absorb moisture when they come in contact with water. In the present case, the sample preparation is stable and there is no effect of water has been observed since the sample resist water perfectly so that these are an eligible candidate for the 3D printing of biosensors/biomedical sensors. • Since the shape recovery is observed excellent (up to 99.58%) in all compounds so that these composite materials are good to apply in actuators, and customized actuators can be 3D printed.

Declaration of Competing Interest The authors report no declarations of interest. Acknowledgement The authors are highly thankful to Centre for manufacturing research, GNDEC, Ludhiana, university centre for research and devel­ opment, Chandigarh University, and department of physics, Chandigarh University, for providing financial/technical assistance to carry out the research work. References [1] Mohammed L, Ansari MN, Pua G, Jawaid M, Islam MS. A review on natural fiber reinforced polymer composite and its applications. International Journal of Polymer Science. 2015;2015. [2] Chatterjee A. Effect of nanoTiO2 addition on poly (methyl methacrylate): an exciting nanocomposite. J Appl Polym Sci 2010;116:3396–407. [3] Li YJ, Duan R, Shi PB, Qin GG. Synthesis of ZnO nanoparticles on Si substrates using a ZnS source. J Cryst Growth 2004;260:309–15. [4] Zeng D, Xie C, Zhu B, Song W, Wang A. Synthesis and characteristics of Sb-doped ZnO nanoparticles. Mater Sci Eng B 2003;104:68–72. [5] Yang Y, Chen H, Zhao B, Bao X. Size control of ZnO nanoparticles via thermal decomposition of zinc acetate coated on organic additives. J Cryst Growth 2004; 263:447–53.

281

R. Kumar et al.

Journal of Manufacturing Processes 60 (2020) 268–282 [30] Fratini L, Ragai I, Wang L. New Trends in Manufacturing Processes Research 2020. Journal of Manufacturing Processes. 2020;(April). https://doi.org/10.1016/j. jmapro.2020.04.017. [31] Mahmoudi M, Tapia G, Franco B, Ma J, Arroyave R, Karaman I, Elwany A. On the printability and transformation behavior of nickel-titanium shape memory alloys fabricated using laser powder-bed fusion additive manufacturing. Journal of Manufacturing Processes. 2018;35(October):672–80. [32] Prabu SM, Perugu CS, Madhu HC, Jangde A, Khan S, Jayachandran S, Manikandan M, Kumar PA, Kailas SV, Palani IA. Exploring the functional and corrosion behavior of friction stir welded NiTi shape memory alloy. Journal of Manufacturing Processes. 2019;47(November):119–28. [33] Estelle K, Blair D, Evans K, Gozen BA. Manufacturing of smart composites with hyperelastic property gradients and shape memory using fused deposition. Journal of Manufacturing Processes. 2017;28(August):500–7. [34] Senatov FS, Niaza KV, Zadorozhnyy MY, Maksimkin AV, Kaloshkin SD, Estrin YZ. Mechanical properties and shape memory effect of 3D-printed PLA-based porous scaffolds. Journal of the mechanical behavior of biomedical materials 2016;57 (April):139–48. [35] Senatov FS, Zadorozhnyy MY, Niaza KV, Medvedev VV, Kaloshkin SD, Anisimova NY, Kiselevskiy MV, Yang KC. Shape memory effect in 3D-printed scaffolds for self-fitting implants. European Polymer Journal. 2017;93(August): 222–31. [36] Choong YY, Maleksaeedi S, Eng H, Wei J, Su PC. 4D printing of high performance shape memory polymer using stereolithography. Materials & Design. 2017;126 (July):219–25. [37] Wu J, Yuan C, Ding Z, Isakov M, Mao Y, Wang T, Dunn ML, Qi HJ. Multi-shape active composites by 3D printing of digital shape memory polymers. Scientific reports 2016;6(April):24224. [38] Zhang Q, Zhang K, Hu G. Smart three-dimensional lightweight structure triggered from a thin composite sheet via 3D printing technique. Scientific reports 2016;6 (February):22431. [39] Matsuzaki R, Ueda M, Namiki M, Jeong TK, Asahara H, Horiguchi K, Nakamura T, Todoroki A, Hirano Y. Three-dimensional printing of continuous-fiber composites by in-nozzle impregnation. Scientific reports 2016;6(March):23058. [40] Liu Y, Hao M, Chen Z, Liu L, Liu Y, Yang W, Ramakrishna S. A Review on Recent Advances in Application of Electrospun Nanofiber Materials as Biosensors. Current Opinion in Biomedical Engineering 2020;(March). https://doi.org/10.1016/j. cobme.2020.02.00119. [41] Emami-Karvani Z, Chehrazi P. Antibacterial activity of ZnO nanoparticle on grampositive and gram-negative bacteria. Afr J Microbiol Res. 2011;5(June (12)): 1368–73. [42] Zhao G, Chen WN. Biofouling formation and structure on original and modified PVDF membranes: Role of microbial species and membrane properties. RSC advances. 2017;7(60):37990–8000. [43] Osman DA, Mustafa MA. Synthesis and characterization of zinc oxide nanoparticles using zinc acetate dihydrate and sodium hydroxide. Journal of Nanoscience and Nanoengineering. 2015;1(4):248–51. [44] Meijer HE, Elemans PH. The modeling of continuous mixers. Part I: The corotating twin-screw extruder. Polymer Engineering & Science 1988;28(March (5)):275–90. [45] Su B, Xie F, Li M, Corrigan PA, Yu L, Li X, Chen L. Extrusion processing of starch film. International Journal of Food Engineering. 2009;5(March (1)). [46] Chapter 4.Data Processing and Analysis, http://gwyddion.net/documentation/ user-guide-en/filters.html#:~:text=First%20it%20smooths%20the%20image,to% 20estimate%20the%20sharper%20image.&text=More%20advanced%20denoising %20functions%20in,in%20section%20Extended%20Data%20Editing., retrieved on 5th June 2020. [47] Andreola F, Leonelli C, Romagnoli M. Techniques Used to Determine Porosity. American Ceramic Society Bulletin. 2000;79(7):49–52. [48] Engineering archives, http://www.engineeringarchives.com/les_mom_modulu softoughness.html, retrieved on 5th June 2020. [49] Keeble M. Error and Uncertainty in Metallographic Measurement. In100 Years of E04 Development of Metallography Standards. March. ASTM International; 2019. retrieved on 5th June 2020, https://www.buehler.com/assets/solutions/technote s/Error_and_Uncertainty_Metallographic_Measurement_TechNote.pdf. [50] Prakash C, Singh S. On the characterization of functionally graded biomaterial primed through a novel plaster mold casting process. Materials Science and Engineering: C. 2020;100:110654. https://doi.org/10.1016/j.msec.2020.110654.

[6] Bodkhe S, Turcot G, Gosselin FP, Therriault D. One-step solvent evaporationassisted 3D printing of piezoelectric PVDF nanocomposite structures. ACS applied materials & interfaces 2017;9(June (24)):20833–42. [7] Dudek PF. FDM 3D printing technology in manufacturing composite elements. Archives of Metallurgy and Materials 2013;58(December (4)):1415–8. [8] Achaby M El, Arrakhiz FZ, Vaudreuil S, Essassi EM, Qaiss A. Piezoelectricpolymorph formation and properties enhancement in graphene oxide–PVDF nanocomposite films. Appl. Surf. Sci. 2012;258:7668–77. [9] Andre B, Clot J, Partouche E, Simonne JJ. Thin film PVDF sensors applied to high acceleration measurements. Sens. Actuators A 1992;33:111–4. [10] Sharma T, Je S, Gill B, Zhang JXJ. Patterning piezoelectric thin film PVDF–TrFE based pressure sensor for catheter application. Sens. Actuators A 2012;177:87–92. [11] Charif AC, Diorio N, Fodor-Csorba K, Puskas JE, Ja kli A. A piezoelectric thermoplastic elastomer containing a bent-core liquid crystal. RSC Adv. 2013;3:17446. [12] Janiczek T, Janiczek J. Linear dynamic system identification in the frequency domain using fractional derivatives. Metrol. Meas. Syst. 2010;2:279–88. [13] Kim D, Hong S, Li D, Seok Roh H, Ahn G, Kim J, Park M, Hong J, Sung T. K. No, A spring-type piezoelectric energy harvester. RSC Adv. 2013;3:3194. [14] Tavares CJ, Marques SM, Rebouta L, Lanceros-Mendez S, Sencadas V, Costa CM, Alves E, Fernandes AJ. PVD-grown photocatalytic TiO2thin films on PVDF substrates for sensors and actuators applications. Thin Solid Films 2008;517: 1161–6. [15] Cardoso VF, Mina G, Costa CM, Tavares CJ, Lanceros-Mendez S. Micro and nanofilms of poly(vinylidene fluoride) with controlled thickness, morphology and electroactive crystalline phase for sensor and actuator applications. Smart Mater. Struct. 2011;20:087002. [16] Chandrana C, Talman J, Pan T, Roy S, Fleischman A. Design and analysis of MEMS based PVDF ultrasonic transducers for vascular imaging. Sensors 2010;10: 8740–50. [17] Giakoumaki AN, Kenanakis G, Klini A, Androulidaki M, Viskadourakis Z, Farsari M, Selimis A. 3D micro-structured arrays of ZnO nanorods. Scientific reports 2017;7 (May (1)):2100. [18] Son S, Jung PH, Park J, Chae D, Huh D, Byun M, Ju S, Lee H. Customizable 3Dprinted architecture with ZnO-based hierarchical structures for enhanced photocatalytic performance. Nanoscale 2018;10(46):21696–702. [19] Zhu Y, Liu K, Deng J, Ye J, Ai F, Ouyang H, Wu T, Jia J, Cheng X, Wang X. 3D printed zirconia ceramic hip joint with precise structure and broad-spectrum antibacterial properties. International journal of nanomedicine 2019;14:5977. [20] Viskadourakis Z, Sevastaki M, Kenanakis G. 3D structured nanocomposites by FDM process: a novel approach for large-scale photocatalytic applications. Applied Physics A. 2018;124(September (9)):585. [21] Zhu Y, Liu K, Deng J, Ye J, Ai F, Ouyang H, Wu T, Jia J, Cheng X, Wang X. 3D printed zirconia ceramic hip joint with precise structure and broad-spectrum antibacterial properties. International journal of nanomedicine 2019;14:5977. [22] Feng P, Wei P, Shuai C, Peng S. Characterization of mechanical and biological properties of 3-D scaffolds reinforced with zinc oxide for bone tissue engineering. PloS one 2014;9(January (1)):e87755. [23] Singh R, Ranjan N. Experimental investigations for preparation of biocompatible feedstock filament of fused deposition modeling (FDM) using twin screw extrusion process. Journal of Thermoplastic Composite Materials. 2018;31(November (11)): 1455–69. [24] Wendaal R. Twin screw extruder. Hanser Publications; 2014. p. 697–8. Ch. 10. [25] Yao C, Li X, Neoh KG, Shi Z, Kang ET. Antibacterial activities of surface modified electrospun poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) fibrous membranes. Applied Surface Science. 2009;255(January (6)):3854–8. [26] Jaleh B, Jabbari A. Evaluation of reduced graphene oxide/ZnO effect on properties of PVDF nanocomposite films. Applied Surface Science. 2014;320(November): 339–47. [27] Tang E, Cheng G, Ma X. Preparation of nano-ZnO/PMMA composite particles via grafting of the copolymer onto the surface of zinc oxide nanoparticles. Powder Technology. 2006;161(February (3)):209–14. [28] Singh S, Singh G, Prakash C, Ramakrishna S. Current status and future directions of fused filament fabrication. Journal of Manufacturing Processes. 2020;55(July): 288–306. [29] Singh S, Ramakrishna S, Singh R. Material issues in additive manufacturing: A review. Journal of Manufacturing Processes. 2017;25(January):185–200.

282